Triple
T6255908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hasselwerder |
E140163
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Reinickendorf |
E14596
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Reinickendorf | Statement: [Hasselwerder, partOf, Reinickendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Reinickendorf Context triple: [Hasselwerder, partOf, Reinickendorf]
-
A.
Reinickendorf
chosen
Reinickendorf is a borough in the northwest of Berlin, Germany, known for its mix of residential neighborhoods, industrial areas, and green spaces including parts of Lake Tegel.
-
B.
Pankow
Pankow is a northeastern borough of Berlin known for its mix of historic neighborhoods, green spaces, and the popular district of Prenzlauer Berg.
-
C.
Treptow-Köpenick
Treptow-Köpenick is Berlin’s largest and greenest borough, known for its extensive forests, lakes, and historic town centers such as Köpenick.
-
D.
Neukölln
Neukölln is a diverse, historically working-class district in southern Berlin known for its vibrant multicultural community, nightlife, and rapidly changing urban landscape.
-
E.
Steglitz-Zehlendorf
Steglitz-Zehlendorf is a borough in southwestern Berlin known for its affluent residential areas, lakes and forests, and historically significant sites such as the Wannsee Conference villa.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063653910819095f1dc3b90ce77db |
completed | March 22, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c63859155881908767074ea315198c |
completed | March 27, 2026, 7:57 a.m. |
Created at: March 22, 2026, 4:24 p.m.